blueReinforcement Learning (RL) [1]}, by inception, addresses sequential decision-making by means of maximising a cumulative reward signal (indicator of how effective a decision was at the previous step), where the choice of action at each step influences not only the immediate reward but also subsequent states, and by virtue of that, also future rewards. Most importantly, RL provides a model-free
framework and solves a discrete-time optimal control
problem cast as an MDP.
